webrtc/talk/base/rollingaccumulator.h

/*
 * libjingle
 * Copyright 2011, Google Inc.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 *  1. Redistributions of source code must retain the above copyright notice,
 *     this list of conditions and the following disclaimer.
 *  2. Redistributions in binary form must reproduce the above copyright notice,
 *     this list of conditions and the following disclaimer in the documentation
 *     and/or other materials provided with the distribution.
 *  3. The name of the author may not be used to endorse or promote products
 *     derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
 * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
 * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
 * EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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 */

#ifndef TALK_BASE_ROLLINGACCUMULATOR_H_
#define TALK_BASE_ROLLINGACCUMULATOR_H_

#include <vector>

#include "talk/base/common.h"

namespace talk_base {

// RollingAccumulator stores and reports statistics
// over N most recent samples.
//
// T is assumed to be an int, long, double or float.
template<typename T>
class RollingAccumulator {
 public:
  explicit RollingAccumulator(size_t max_count)
    : samples_(max_count) {
    Reset();
  }
  ~RollingAccumulator() {
  }

  size_t max_count() const {
    return samples_.size();
  }

  size_t count() const {
    return count_;
  }

  void Reset() {
    count_ = 0U;
    next_index_ = 0U;
    sum_ = 0.0;
    sum_2_ = 0.0;
    max_ = T();
    max_stale_ = false;
    min_ = T();
    min_stale_ = false;
  }

  void AddSample(T sample) {
    if (count_ == max_count()) {
      // Remove oldest sample.
      T sample_to_remove = samples_[next_index_];
      sum_ -= sample_to_remove;
      sum_2_ -= sample_to_remove * sample_to_remove;
      if (sample_to_remove >= max_) {
        max_stale_ = true;
      }
      if (sample_to_remove <= min_) {
        min_stale_ = true;
      }
    } else {
      // Increase count of samples.
      ++count_;
    }
    // Add new sample.
    samples_[next_index_] = sample;
    sum_ += sample;
    sum_2_ += sample * sample;
    if (count_ == 1 || sample >= max_) {
      max_ = sample;
      max_stale_ = false;
    }
    if (count_ == 1 || sample <= min_) {
      min_ = sample;
      min_stale_ = false;
    }
    // Update next_index_.
    next_index_ = (next_index_ + 1) % max_count();
  }

  T ComputeSum() const {
    return static_cast<T>(sum_);
  }

  double ComputeMean() const {
    if (count_ == 0) {
      return 0.0;
    }
    return sum_ / count_;
  }

  T ComputeMax() const {
    if (max_stale_) {
      ASSERT(count_ > 0 &&
          "It shouldn't be possible for max_stale_ && count_ == 0");
      max_ = samples_[next_index_];
      for (size_t i = 1u; i < count_; i++) {
        max_ = _max(max_, samples_[(next_index_ + i) % max_count()]);
      }
      max_stale_ = false;
    }
    return max_;
  }

  T ComputeMin() const {
    if (min_stale_) {
      ASSERT(count_ > 0 &&
          "It shouldn't be possible for min_stale_ && count_ == 0");
      min_ = samples_[next_index_];
      for (size_t i = 1u; i < count_; i++) {
        min_ = _min(min_, samples_[(next_index_ + i) % max_count()]);
      }
      min_stale_ = false;
    }
    return min_;
  }

  // O(n) time complexity.
  // Weights nth sample with weight (learning_rate)^n. Learning_rate should be
  // between (0.0, 1.0], otherwise the non-weighted mean is returned.
  double ComputeWeightedMean(double learning_rate) const {
    if (count_ < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) {
      return ComputeMean();
    }
    double weighted_mean = 0.0;
    double current_weight = 1.0;
    double weight_sum = 0.0;
    const size_t max_size = max_count();
    for (size_t i = 0; i < count_; ++i) {
      current_weight *= learning_rate;
      weight_sum += current_weight;
      // Add max_size to prevent underflow.
      size_t index = (next_index_ + max_size - i - 1) % max_size;
      weighted_mean += current_weight * samples_[index];
    }
    return weighted_mean / weight_sum;
  }

  // Compute estimated variance.  Estimation is more accurate
  // as the number of samples grows.
  double ComputeVariance() const {
    if (count_ == 0) {
      return 0.0;
    }
    // Var = E[x^2] - (E[x])^2
    double count_inv = 1.0 / count_;
    double mean_2 = sum_2_ * count_inv;
    double mean = sum_ * count_inv;
    return mean_2 - (mean * mean);
  }

 private:
  size_t count_;
  size_t next_index_;
  double sum_;    // Sum(x) - double to avoid overflow
  double sum_2_;  // Sum(x*x) - double to avoid overflow
  mutable T max_;
  mutable bool max_stale_;
  mutable T min_;
  mutable bool min_stale_;
  std::vector<T> samples_;

  DISALLOW_COPY_AND_ASSIGN(RollingAccumulator);
};

}  // namespace talk_base

#endif  // TALK_BASE_ROLLINGACCUMULATOR_H_